A. Field of The Invention
The present invention relates generally to a new cardiac tachyarrhythmia detection system and method, which is suitable for use in cardiac therapy and monitoring equipment and other similar devices which incorporate techniques of ECG processing and analysis. These devices may include out-of-hospital or in-hospital, external automatic defibrillator (AED), implantable cardioverter defibrillator (ICD), pacemakers, and other similar systems. More particularly, the present invention relates to a system and method capable of discriminating ventricular fibrillation (VF), ventricular tachycardia (VT), non-shockable tachyarrhythmia, and high-frequency noise, using an ECG complexity measure CM.
B. Description of The Prior Art
Despite major advances in the diagnosis and treatment of heart disease over the past decades, a substantial number (350,000 in the USA) of patients each year suffer sudden cardiac arrest (SCA) due to, for example, ventricular tachycardia (VT) or ventricular fibrillation (VF). However, the national survival rate of SCA is merely about 5%. The standard therapy for SCA is early cardioversion/defibrillation either by implantable cardioverter defibrillators (ICD) or by automatic external defibrillator (AED). An important parameter that affects the reliability and accuracy of these therapies is the algorithm or technique used to detect shockable VT and VF and while avoiding unnecessary shocks possibly caused by non-shockable tachyarrhythmias (e.g. supraventricular tachycardia (SVT), atrial fibrillation (AF), etc.) and some high-frequency noise commonly encountered under practical situations.
Since electrical shocks always have adverse affects on the myocardium, another primary goal of all cardiac therapies is to minimize the number and energy level of electrical signals delivered to the patient. To this end, VT, which requires much lower energy levels for effective therapy, must be effectively differentiated from VF. Moreover, the safety of a device, as well as its ease of use, extent of automatic operation, and widespread acceptance also depend on the performance of the arrhythmia detection system and method.
All devices and systems monitoring the cardiac state of a patient and/or generating antitachyarrhythmia therapy rely on the analysis of the electrocardiogram (ECG) from the patient. The analyses proposed and used so far were based on manipulation of information in the time-domain, frequency-domain, time-frequency domain, and bispectral domain, and even nonlinear dynamics domain. However, all these manipulations have fundamental limitations associated with the linear nature, computational complexity, or difficulty in real-time implementation as well as low sensitivity and specificity. For this reason, currently, the percentage of patients with ICDs who are paced or shocked unnecessarily exceeds 40%. Similarly, AEDs are only 90% effective or sufficiently sensitive to detecting ventricular tachyarrhythmia and 90-95% accurate in detecting and correctly classifying other heart rhythms. Moreover, discrimination of VT from VF is still a difficult object to achieve using existing algorithms for ICD and AED. Therefore, a need still exists for a simple and effective arrhythmia detection system and method using sophisticated signal processing techniques.
It has been found that the electrical activity of the heart is best described as by a non-linear dynamical system. The theory used to describe such systems is known as non-linear dynamics theory, which can be used therefore to analyze the dynamic mechanisms underlying the cardiac activities. Dynamical systems such as the heart can exhibit both periodic and chaotic behaviors depending on certain system parameters. For instance, VF is a highly complex, seemingly random phenomenon, and can be described as chaotic cardiac behavior. Therefore, a diagnostic system with the ability to quantify abnormalities of a non-linear dynamic cardiac system would be expected to have an enhanced performance. In fact, methods have been described which were derived from nonlinear dynamics in ECG signal processing and arrhythmia prediction and detection. For example, Poincare map or return map of the ECG amplitude for cardiac fibrillation detection was disclosed in U.S. Pat. No. 5,439,004, issued to Duong-Van. U.S. Pat. No. 5,643,325, issued to Karagueuzian et al., disclosed the degree of deterministic chaos in phase-plane plot may indicate a propensity for fibrillation including both the risk of fibrillation and the actual onset of fibrillation. A method for detecting a heart disorder using correlation dimension (by Grassberger-Procaccia algorithm) was also disclosed in U.S. Pat. No. 5,643,325, incorporated herein by reference. A slope filtered point-wise correlation dimension algorithm is utilized to predict imminent fibrillation, as disclosed in U. S. Pat. No. 5,425,749, issued to Adams, and xe2x80x9cSlope Filtered Pointwise Correlation Dimension Algorithm and Evaluation with Prefibrillation Heart Rate Data,xe2x80x9d were disclosed in the Journal of Electrocardiology, Vol. 24, Supplement, pp. 97-101, authored by Kroll and Fulton. These non-linear dynamics derived methods are based on the phase space reconstruction, and the computational demand and complexity are considerable for current ICD and AED, therefore, they are still difficult to apply in the real world.
The cardiac electrical signal is the complex resultant of a plurality of spatial and temporal inputs and many non-linear dynamic features or characteristics should be expected in this signal, such as different spatio-temporal patterns manifested in the ECG. One such dynamic feature is xe2x80x98complexity.xe2x80x99 Different non-linear dynamic cardiac behavior is associated with different degrees of complexity. Therefore, the measure characterizing complexity can be used as an effective tool for detecting VT and VF. Correlation dimension and approximate entropy have been proposed as means of characterizing complexity, however, these approaches requires highly accurate calculations involving long data segments and are very time-consuming (See Caswell Schuckers S. A., xe2x80x9cApproximate Entropy as a Measure of Morphologic Variability for Ventricular Tachycardia and Fibrillationxe2x80x9d, Computers in Cardiology, 1998, 25:265-268). Hence, these approaches cannot be extended to real-time application in ICD and AED. Another method of complexity analysis was proposed by Lempel and Ziv in xe2x80x9cOn the Complexity of Finite Sequencesxe2x80x9d, IEEE Trans. Information Theory, 1976, IT-22: 75-81. Zhang et al. have disclosed some results of this kind of complexity analysis for normal sinus rhythm, VT, and VF, in xe2x80x9cDetecting Ventricular Tachycardia and Fibrillation by Complexity Measurexe2x80x9d, IEEE Transactions on Biomedical Engineering, 1999, 46: 548-555. All the above-mentioned references are incorporated herein by reference. However, none of these references mention the way to perform real-time complexity analysis in ICDs and AEDs. Moreover, none of these references discuss a method that can be used to avoid unnecessary therapy caused by SVT or high-frequency noise.
In view of the clinical importance of ventricular conditions, more emphasis should be put on the analysis and feature extraction of the ventricular electrical activity, manifested as QRS complex on the ECG. By an optimized threshold method, the xe2x80x9ccomplexityxe2x80x9d of ventricular activity patterns can be analyzed quantitatively by complexity analysis after transforming it into a character sequence. The cardiac tachyarrhythmia detection system and method of present invention as disclosed herein is simple, computationally efficient, effective, robust and reliable, and well suited for real-time implementation and, at the same time, it has immunity ability to noise and artifacts. Therefore, it offers all the desirable features for the practical application in AED and ICD.
The present invention fulfills the need in AEDs and ICDs by providing a cardiac arrhythmia detection system and method, which provides a clearer and more reliable indication of the onset of VT and VF that has been available in the prior arts and, at the same time, avoids possible misidentifications caused by SVT, high-frequency artifacts or noise.
It is, accordingly, an objective of the present invention to provide an improved system and method for simultaneously detecting shockable VT and VF, and discriminating non-shockable SVT, and high-frequency artifacts and noise as well.
A further object of the present invention is to provide such a detection system and method which is capable of correctly and accurately distinguishing in real time different kinds of cardiac episodes (shockable and non-shockable) using an easy-to-implement algorithm without computation complexity.
It is an additional object of the present invention to utilize the dynamical non-linear nature of the heart as exhibited in ECG signals to detect VT and VF by quantitative measurements thereof using non-linear dynamic theory.
It is still another further object of the present invention to provide such a detection system and method, which discriminates VT from VF and thereby allows the application of lower-energy cardioversion therapy for VT to provide significant energy savings for the battery powered device and improved patient comfort, such as an implantable cardioverter/defibrillator, high-energy defibrillation therapy for VF and, at the same time, avoiding unnecessary shock for SVT.
These and other objects of the invention are realized by providing a novel cardiac tachyarrhythmia detection system and method as described in more detail below. More specifically, the present invention pertains to a method and apparatus which uses the complexity measure of a nonlinear dynamical system associated with cardiac rhythms to detect VT, VF, SVT and high-frequency noise.
As discussed above, the non-linear dynamic cardiac activity has different characteristics for different cardiac rhythms. These characteristics are apparent from the different spatio-temporal patterns in the corresponding ECG waveforms with different complexity. In accordance with this invention, the complexity of these patterns is quantified and designated as a complexity measure CM. The CM, calculated by a complexity analysis, has a value between 0 to 100. The larger CM value, the more complex the activity. For non-shockable tachyarrhythmias (such as SVT and atrial fibrillation (AF)), the corresponding CM is lower than for VT and VF since, during SVT and AF, the cardiac activity has a regular, more organized, and periodic state. CM has a lower value for VT than for VF, since cardiac activity during VT is more stable and the ventricular activity (QRS complexes) basically looks more similar than that of VF. The high-frequency artifacts or noise manifest random-like activity, therefore, the corresponding CM values are much higher than the values for cardiac activities.
By directly analyzing the non-linear dynamics representing cardiac activities and quantitatively characterizing the associated complexity as a parameter CM, the detection system and method of the present invention is able to overcome the limitations of existing methods and is capable of discriminating accurately between VT, VF, SVT, and high-frequency noise.
This invention offers a considerable improvement over current methods of analysis by employing a new criterion for reliable separation among VT, VF, SVT, and high-frequency noise. Particularly, by using three CM thresholds (i.e. low complexity threshold (LCT), mediate complexity threshold (MCT), and high complexity threshold (HCT)), different kinds of tachyarrhythmia (i.e. heart rate (HR) above a pre-set rate threshold) and high-frequency noise are discriminated from each other: for non-shockable tachyarrhythmia, CMxe2x89xa6LCT; for VT, LCT less than CMxe2x89xa6MCT; for VF, MCT less than CMxe2x89xa6HCT; and for high-frequency noise, HCT less than CM. In this way, the arrhythmia is determined accurately and rapidly allowing the device or system to select the corresponding therapy, if required.
Moreover, since VT, VF, SVT, and high-frequency noise are accurately detected in appropriate shock treatment and lower energy consumption is achieved.